On the Dynamics of Belief Correction
with Bryan Kelly, Semyon Malamud, and Emil Siriwardane
Revise and Resubmit, Quarterly Journal of Economics
Abstract: We introduce Belief Correction Curves (BCCs), a set of moments that characterize how deviations from full-information rational expectations (FIRE) are corrected over time. BCCs generalize and sharpen standard tests of FIRE by imposing restrictions on the path of forecast errors for a fixed target following a revision. Empirically, professional forecasts exhibit pervasive overreaction followed by slow and smooth corrections. Leading behavioral models struggle to match these patterns because they imply corrections that are too abrupt or too tied to the degree of overreaction. We build a new model of diagnostic expectations that fits the data substantially better by exploiting a fundamental link between memory and correction speed. The model's memory parameter varies strongly across forecasters and targets, consistent with context-dependent recall.
Credit Information in Earnings Calls
with Harry Mamaysky, and Yiwen Shen
Forthcoming, Journal of Banking and Finance
Abstract: We develop a novel technique to extract credit-relevant information from the text of quarterly earnings calls. This information is not spanned by fundamental or market variables and forecasts future credit spread changes. One reason for such forecastability is that our text-based measure predicts spread risk and firm fundamentals. More firm- and call-level complexity, and higher analyst disagreement, increase the forecasting power of our measure for spread changes. Out-of-sample portfolio tests show the information in our measure is valuable for investors. Our results suggest investors do not fully internalize the credit-relevant information in earnings calls.
